Primary Menu

EHR are useful in pre-determining mortality and risk of sepsis

Sepsis is one of the most common causes of death and hospitalization in the US. Prompt diagnosis and treatment is essential to the management plan of this medical condition. Widespread sepsis is a systemic inflammatory physiologic response of the body to infection which damages vital organs and affects their functions. The damages can cause temporary or permanent disabilities. Sepsis is known to be associated to the accumulation of excessive lactate in the body which in turn is due to the lack of oxygen for metabolism.

In Sacramento, California, a group of researchers from UC Davis found that certain data found in the electronic health records of patients can be used to assess sepsis risk and predict mortality from the disease. Important information taken in the hospital as routine such as the patient’s vital signs, i.e., blood pressure, temperature and respiratory rate, and white cell count can be used to predict the patient’s risk for developing sepsis. Likewise, it was found that the lactate levels of the patient can be utilized to determine the probability of mortality from the disease. Through these findings, probable cases can be precisely identified and sepsis prevention and prompt treatment can be initiated.

Tim Albertson, chair of UC Davis Department of Internal Medicine and one of the authors of the research, points out that through the study, they have shown that electronic health records can be used to pinpoint favorable and likewise disapproving practices in the medical field which helps to improve the treatment and management of disease entities. This in turn can improve the survival rate of patients from morbid diseases.

The research was conducted by analyzing data from health records of 741 patients diagnosed with sepsis in the year 2010 at UC Davis Medical Center. The study, which was published in the Journal of the American Medical Informatics Association, is entitled “From Vital Signs to Clinical Outcomes for Patients with Sepsis: A Machine Learning Basis for a Clinical Decision Support System.”

The team is currently developing in an algorithm which automatically ‘calculates’ the risk of a patient to develop sepsis based on the data derived from their electronic health records. Assistant Professor Ilias Tagkopoulos from UC Davis, a co-author of the study, explains that one of the benefits of the algorithm is that it can aid physicians in decision-making since the system has the ability to utilize the patient’s data and identify their health status and predict health outcomes. This, he says is more advantageous for doctors, “rather than using a ‘gut-level’ approach in an uncertain situation.”